Project acronym BROKERS
Project Participatory Urban Governance between Democracy and Clientelism: Brokers and (In)formal Politics
Researcher (PI) Martijn Koster
Host Institution (HI) STICHTING KATHOLIEKE UNIVERSITEIT
Call Details Starting Grant (StG), SH2, ERC-2015-STG
Summary The emergence of participatory governance has resulted in the delegation of governmental responsibilities to citizens. Individuals position themselves as voluntary mediators, or brokers, between the government and their fellow citizens. This research asks: what are the roles of such brokers in participatory urban governance, and how do they influence democratic governance? This study will investigate ethnographically how brokers position themselves in administrative schemes, and examine the formal and informal dimensions of their performance. It will analyse the practices, discourses and networks, both in and out of officially sanctioned channels and government institutions. The research approaches brokers as ‘assemblers’, connective agents who actively bring together different governmental and citizen actors, institutions and resources.
The scholarly debate on brokerage within participatory governance is divided into two different arguments: first, an argument about neoliberal deregulation located in the Global North, which encourages the practices of active citizen-mediators, and second, a modernization argument in the Global South, which sees brokers as remnants of a clientelist political system. This research will combine these arguments to study settings in both the North and the South. It employs a comparative urbanism design to study four cities that are recognized as pioneers in democratic participatory governance, two in the North and two in the South: Rotterdam (NL), Manchester (UK), Cochabamba (Bolivia) and Recife (Brazil).
This research builds upon theories from political anthropology, urban studies, citizenship studies and public administration to develop a new framework for analysing brokerage in participatory urban governance. Understanding how the formal and informal dimensions of participatory governance are entwined will contribute to our ability to theorize the conditions under which this type of governance can give rise to more democratic cities.
Summary
The emergence of participatory governance has resulted in the delegation of governmental responsibilities to citizens. Individuals position themselves as voluntary mediators, or brokers, between the government and their fellow citizens. This research asks: what are the roles of such brokers in participatory urban governance, and how do they influence democratic governance? This study will investigate ethnographically how brokers position themselves in administrative schemes, and examine the formal and informal dimensions of their performance. It will analyse the practices, discourses and networks, both in and out of officially sanctioned channels and government institutions. The research approaches brokers as ‘assemblers’, connective agents who actively bring together different governmental and citizen actors, institutions and resources.
The scholarly debate on brokerage within participatory governance is divided into two different arguments: first, an argument about neoliberal deregulation located in the Global North, which encourages the practices of active citizen-mediators, and second, a modernization argument in the Global South, which sees brokers as remnants of a clientelist political system. This research will combine these arguments to study settings in both the North and the South. It employs a comparative urbanism design to study four cities that are recognized as pioneers in democratic participatory governance, two in the North and two in the South: Rotterdam (NL), Manchester (UK), Cochabamba (Bolivia) and Recife (Brazil).
This research builds upon theories from political anthropology, urban studies, citizenship studies and public administration to develop a new framework for analysing brokerage in participatory urban governance. Understanding how the formal and informal dimensions of participatory governance are entwined will contribute to our ability to theorize the conditions under which this type of governance can give rise to more democratic cities.
Max ERC Funding
1 497 570 €
Duration
Start date: 2016-08-01, End date: 2021-07-31
Project acronym CAFES
Project Causal Analysis of Feedback Systems
Researcher (PI) Joris Marten Mooij
Host Institution (HI) UNIVERSITEIT VAN AMSTERDAM
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making.
The key challenge that will be addressed is how to deal with cyclic causal relationships ("feedback loops"). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.
Summary
Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making.
The key challenge that will be addressed is how to deal with cyclic causal relationships ("feedback loops"). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.
Max ERC Funding
1 405 652 €
Duration
Start date: 2015-09-01, End date: 2020-08-31
Project acronym CCC
Project Cracking the Cerebellar Code
Researcher (PI) Christiaan Innocentius De Zeeuw
Host Institution (HI) ERASMUS UNIVERSITAIR MEDISCH CENTRUM ROTTERDAM
Call Details Advanced Grant (AdG), LS5, ERC-2011-ADG_20110310
Summary Spike trains transfer information to and from neurons. Most studies so far assume that the average firing rate or “rate coding” is the predominant way of information coding. However, spikes occur at millisecond precision, and their actual timing or “temporal coding” can in principle strongly increase the information content of spike trains. The two coding mechanisms are not mutually exclusive. Neurons may switch between rate and temporal coding, or use a combination of both coding mechanisms at the same time, which would increase the information content of spike trains even further. Here, we propose to investigate the hypothesis that temporal coding plays, next to rate coding, important and specific roles in cerebellar processing during learning. The cerebellum is ideal to study this timely topic, because it has a clear anatomy with well-organized modules and matrices, a well-described physiology of different types of neurons with distinguishable spiking activity, and a central role in various forms of tractable motor learning. Moreover, uniquely in the brain, the main types of neurons in the cerebellar system can be genetically manipulated in a cell-specific fashion, which will allow us to investigate the behavioural importance of both coding mechanisms following cell-specific interference and/or during cell-specific visual imaging. Thus, for this proposal we will create conditional mouse mutants that will be subjected to learning paradigms in which we can disentangle the contributions of rate coding and temporal coding using electrophysiological and optogenetic recordings and stimulation. Together, our experiments should elucidate how neurons in the brain communicate during natural learning behaviour and how one may be able to intervene in this process to affect or improve procedural learning skills.
Summary
Spike trains transfer information to and from neurons. Most studies so far assume that the average firing rate or “rate coding” is the predominant way of information coding. However, spikes occur at millisecond precision, and their actual timing or “temporal coding” can in principle strongly increase the information content of spike trains. The two coding mechanisms are not mutually exclusive. Neurons may switch between rate and temporal coding, or use a combination of both coding mechanisms at the same time, which would increase the information content of spike trains even further. Here, we propose to investigate the hypothesis that temporal coding plays, next to rate coding, important and specific roles in cerebellar processing during learning. The cerebellum is ideal to study this timely topic, because it has a clear anatomy with well-organized modules and matrices, a well-described physiology of different types of neurons with distinguishable spiking activity, and a central role in various forms of tractable motor learning. Moreover, uniquely in the brain, the main types of neurons in the cerebellar system can be genetically manipulated in a cell-specific fashion, which will allow us to investigate the behavioural importance of both coding mechanisms following cell-specific interference and/or during cell-specific visual imaging. Thus, for this proposal we will create conditional mouse mutants that will be subjected to learning paradigms in which we can disentangle the contributions of rate coding and temporal coding using electrophysiological and optogenetic recordings and stimulation. Together, our experiments should elucidate how neurons in the brain communicate during natural learning behaviour and how one may be able to intervene in this process to affect or improve procedural learning skills.
Max ERC Funding
2 499 600 €
Duration
Start date: 2012-04-01, End date: 2017-03-31
Project acronym CHAMELEON
Project Intuitive editing of visual appearance from real-world datasets
Researcher (PI) Diego Gutierrez Pérez
Host Institution (HI) UNIVERSIDAD DE ZARAGOZA
Call Details Consolidator Grant (CoG), PE6, ERC-2015-CoG
Summary Computer-generated imagery is now ubiquitous in our society, spanning fields such as games and movies, architecture, engineering, or virtual prototyping, while also helping create novel ones such as computational materials. With the increase in computational power and the improvement of acquisition techniques, there has been a paradigm shift in the field towards data-driven techniques, which has yielded an unprecedented level of realism in visual appearance. Unfortunately, this leads to a series of problems, identified in this proposal: First, there is a disconnect between the mathematical representation of the data and any meaningful parameters that humans understand; the captured data is machine-friendly, but not human friendly. Second, the many different acquisition systems lead to heterogeneous formats and very large datasets. And third, real-world appearance functions are usually nonlinear and high-dimensional. As a result, visual appearance datasets are increasingly unfit to editing operations, which limits the creative process for scientists, engineers, artists and practitioners in general. There is an immense gap between the complexity, realism and richness of the captured data, and the flexibility to edit such data.
We believe that the current research path leads to a fragmented space of isolated solutions, each tailored to a particular dataset and problem. We propose a research plan at the theoretical, algorithmic and application levels, putting the user at the core. We will learn key relevant appearance features in terms humans understand, from which intuitive, predictable editing spaces, algorithms, and workflows will be defined. In order to ensure usability and foster creativity, we will also extend our research to efficient simulation of visual appearance, exploiting the extra dimensionality of the captured datasets. Achieving our goals will finally enable us to reach the true potential of real-world captured datasets in many aspects of society.
Summary
Computer-generated imagery is now ubiquitous in our society, spanning fields such as games and movies, architecture, engineering, or virtual prototyping, while also helping create novel ones such as computational materials. With the increase in computational power and the improvement of acquisition techniques, there has been a paradigm shift in the field towards data-driven techniques, which has yielded an unprecedented level of realism in visual appearance. Unfortunately, this leads to a series of problems, identified in this proposal: First, there is a disconnect between the mathematical representation of the data and any meaningful parameters that humans understand; the captured data is machine-friendly, but not human friendly. Second, the many different acquisition systems lead to heterogeneous formats and very large datasets. And third, real-world appearance functions are usually nonlinear and high-dimensional. As a result, visual appearance datasets are increasingly unfit to editing operations, which limits the creative process for scientists, engineers, artists and practitioners in general. There is an immense gap between the complexity, realism and richness of the captured data, and the flexibility to edit such data.
We believe that the current research path leads to a fragmented space of isolated solutions, each tailored to a particular dataset and problem. We propose a research plan at the theoretical, algorithmic and application levels, putting the user at the core. We will learn key relevant appearance features in terms humans understand, from which intuitive, predictable editing spaces, algorithms, and workflows will be defined. In order to ensure usability and foster creativity, we will also extend our research to efficient simulation of visual appearance, exploiting the extra dimensionality of the captured datasets. Achieving our goals will finally enable us to reach the true potential of real-world captured datasets in many aspects of society.
Max ERC Funding
1 629 519 €
Duration
Start date: 2016-11-01, End date: 2021-10-31
Project acronym ChemicalYouth
Project What chemicals do for youths in their everyday lives
Researcher (PI) Anita Petra Hardon
Host Institution (HI) UNIVERSITEIT VAN AMSTERDAM
Call Details Advanced Grant (AdG), SH2, ERC-2012-ADG_20120411
Summary The everyday lives of contemporary youths are awash with chemicals and pharmaceutical compounds to boost pleasure, moods, sexual performance, vitality, appearance and health. Nevertheless, most studies of chemical use among young people have focused on the abuse of specific recreational drugs and their role within deviant youth sub-cultures. Instead of explaining drug abuse with the purpose of controlling it, this project aims to examine the pervasive use of chemicals from the perspectives of youths themselves. It aims to understand what chemical and pharmaceutical substances, and not only illicit narcotics, ‘do’ for youths. How are chemicals a part of their everyday lives? What role do they play in calming their fears or in achieving their dreams and aspirations? How can we understand the ways in which chemicals affect their bodies and minds?
The theoretical innovation promised by this project lies in its combining of disciplines – most notably medical anthropology, science and technology studies and youth studies – to formulate a new groundbreaking framework for understanding the complex sociality of chemicals in youths’ everyday lives. The framework will have both scientific and societal impact.
Ethnographic research will be conducted in four medium-sized cities: Marseille in France, Amsterdam in the Netherlands, Makassar in Indonesia, and Batangas in the Philippines.
Summary
The everyday lives of contemporary youths are awash with chemicals and pharmaceutical compounds to boost pleasure, moods, sexual performance, vitality, appearance and health. Nevertheless, most studies of chemical use among young people have focused on the abuse of specific recreational drugs and their role within deviant youth sub-cultures. Instead of explaining drug abuse with the purpose of controlling it, this project aims to examine the pervasive use of chemicals from the perspectives of youths themselves. It aims to understand what chemical and pharmaceutical substances, and not only illicit narcotics, ‘do’ for youths. How are chemicals a part of their everyday lives? What role do they play in calming their fears or in achieving their dreams and aspirations? How can we understand the ways in which chemicals affect their bodies and minds?
The theoretical innovation promised by this project lies in its combining of disciplines – most notably medical anthropology, science and technology studies and youth studies – to formulate a new groundbreaking framework for understanding the complex sociality of chemicals in youths’ everyday lives. The framework will have both scientific and societal impact.
Ethnographic research will be conducted in four medium-sized cities: Marseille in France, Amsterdam in the Netherlands, Makassar in Indonesia, and Batangas in the Philippines.
Max ERC Funding
2 489 967 €
Duration
Start date: 2013-06-01, End date: 2018-05-31
Project acronym CHILDROBOT
Project Children and social robots: An integrative framework
Researcher (PI) Jochen Peter
Host Institution (HI) UNIVERSITEIT VAN AMSTERDAM
Call Details Consolidator Grant (CoG), SH2, ERC-2015-CoG
Summary Robots used to be made for labor; now, they are increasingly also made for relationships. Social robots can learn from us, teach us, play with us, and assist us. With the market for social robots expected to grow substantially in the next 20 years, social robots are likely to become a life-changing technology similar to personal computers or smart phones. Still, we still know little about one of the most intriguing, relevant, and timely issues in this process – children’s interaction with social robots. Children are not only increasingly recognized and targeted as early adopters of new technologies; they may also be more susceptible to potential effects of interacting with robots than are adults.
As research on child-robot interaction (CRI) is still a fragmented field, the main aim of the proposed project is to develop an integrative framework of CRI. This framework synthesizes theories and concepts from communication research, human-robot-interaction, as well as developmental and social psychology in an entirely new way. It will focus (1) on the antecedents of children’s acceptance of social robots; (2) the con-sequences of CRI for children’s learning of social skills from social robots and their relationship formation with them; and (3) the processes that explain why such effects emerge.
The project combines survey and experimental research, thereby bringing an unprecedented, but much needed multi-methodological approach to the field of CRI. Focusing on 8-9 year-old children, the project will also provide two crucial methodological innovations: (a) the creation an inventory of standardized measures for CRI and (b) new procedures and research designs to study long-term CRI. In its pioneering focus on a disruptive new technology, its theoretically unifying character, and its original methodological contributions, the project will not only define the field of CRI, but will also present a completely new agenda for it.
Summary
Robots used to be made for labor; now, they are increasingly also made for relationships. Social robots can learn from us, teach us, play with us, and assist us. With the market for social robots expected to grow substantially in the next 20 years, social robots are likely to become a life-changing technology similar to personal computers or smart phones. Still, we still know little about one of the most intriguing, relevant, and timely issues in this process – children’s interaction with social robots. Children are not only increasingly recognized and targeted as early adopters of new technologies; they may also be more susceptible to potential effects of interacting with robots than are adults.
As research on child-robot interaction (CRI) is still a fragmented field, the main aim of the proposed project is to develop an integrative framework of CRI. This framework synthesizes theories and concepts from communication research, human-robot-interaction, as well as developmental and social psychology in an entirely new way. It will focus (1) on the antecedents of children’s acceptance of social robots; (2) the con-sequences of CRI for children’s learning of social skills from social robots and their relationship formation with them; and (3) the processes that explain why such effects emerge.
The project combines survey and experimental research, thereby bringing an unprecedented, but much needed multi-methodological approach to the field of CRI. Focusing on 8-9 year-old children, the project will also provide two crucial methodological innovations: (a) the creation an inventory of standardized measures for CRI and (b) new procedures and research designs to study long-term CRI. In its pioneering focus on a disruptive new technology, its theoretically unifying character, and its original methodological contributions, the project will not only define the field of CRI, but will also present a completely new agenda for it.
Max ERC Funding
1 999 012 €
Duration
Start date: 2017-01-01, End date: 2021-12-31
Project acronym CITISENSE
Project Evolving communication systems in response to altered sensory environments
Researcher (PI) Wouter Halfwerk
Host Institution (HI) STICHTING VU
Call Details Starting Grant (StG), LS8, ERC-2018-STG
Summary How animal communication systems evolve is a fundamental question in ecology and evolution and crucial for our understanding of adaptation and speciation. I will make use of the process of urbanization to address how communication signals adapt to changes in the sensory environment. I will focus on the impact of noise and light pollution on acoustic communication of Neotropical frogs and address the following questions:
1) How do senders, such as a male frog, adjust their signals to altered sensory environments? I will assess plasticity and heritability of signal divergence found between urban and forest populations of the tungara frog. 2) How do signals evolve in response to direct (via sender) and indirect (via receivers) selection pressures? I will expose forest sites to noise and light pollution, parse out importance of multiple selection pressures and carry out experimental evolution using artificial phenotypes.
3) What are the evolutionary consequences of signal divergence? I will assess inter-and-intra sexual responses to signal divergence between urban and forest populations. 4) Can we predict how species adapt their signals to the sensory environment? I will use a trait-based comparative approach to study signal divergence among closely related species with known urban populations.
Our state-of-the-art automated sender-receiver system allows for experimental evolution using long-lived species and opens new ways to study selection pressures operating on animal behaviour under real field conditions. Our expected results will provide crucial insight into the early stages of signal divergence that may ultimately lead to reproductive isolation and speciation.
Summary
How animal communication systems evolve is a fundamental question in ecology and evolution and crucial for our understanding of adaptation and speciation. I will make use of the process of urbanization to address how communication signals adapt to changes in the sensory environment. I will focus on the impact of noise and light pollution on acoustic communication of Neotropical frogs and address the following questions:
1) How do senders, such as a male frog, adjust their signals to altered sensory environments? I will assess plasticity and heritability of signal divergence found between urban and forest populations of the tungara frog. 2) How do signals evolve in response to direct (via sender) and indirect (via receivers) selection pressures? I will expose forest sites to noise and light pollution, parse out importance of multiple selection pressures and carry out experimental evolution using artificial phenotypes.
3) What are the evolutionary consequences of signal divergence? I will assess inter-and-intra sexual responses to signal divergence between urban and forest populations. 4) Can we predict how species adapt their signals to the sensory environment? I will use a trait-based comparative approach to study signal divergence among closely related species with known urban populations.
Our state-of-the-art automated sender-receiver system allows for experimental evolution using long-lived species and opens new ways to study selection pressures operating on animal behaviour under real field conditions. Our expected results will provide crucial insight into the early stages of signal divergence that may ultimately lead to reproductive isolation and speciation.
Max ERC Funding
1 500 000 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
Project acronym CLLS
Project Analysing coherence in law through legal scholarship
Researcher (PI) Dave DE RUYSSCHER
Host Institution (HI) STICHTING KATHOLIEKE UNIVERSITEIT BRABANT
Call Details Starting Grant (StG), SH2, ERC-2016-STG
Summary Coherence of law is created in the writings of legal scholars who systematize rules and principles of law. Their pursuit of coherence is vital for the effectiveness of legal systems. However, coherence of law has almost not been analysed in a systematic, empirical way. The project will therefore develop a methodology that will address coherence across forms (‘sources’) of law (legislation, legal scholarship, case law, customs), across themes (e.g. criminal law and contracts) and across authors, and which will additionally encompass interaction with societal demand and contextual factors. The methodology will be ground-breaking because it will disentangle the concept of coherence into measurable modes of interconnectedness, weighing them together so as to assess (in)coherence at the level of the legal system. This methodology will constitute a stepping stone for a new field of dynamic coherence of law created through legal scholarship that will ultimately improve the quality of law. It will be founded on academic writings on law from the early modern period (ca. 1500 - ca. 1800) that concern the theme of collateral rights, that is, those rights facilitating expropriation of the assets of debtors in case of their default. Indications are that the impact of rules on collateral rights hinged on coherence as established in legal writings, and that in the period mentioned legal coherence for this theme was increasing. Coherence in development will be traced in the interpretations of legal scholars following on from interactions between scholarly writings, local law (bylaws, judgments) and commercial practice (contracts). Connections of rules and principles found will be presented in frames of analysis that cluster them along variables of context, time and source of law. The combination of legal analysis with a broad scope of coherence (cross-source, context-driven) will build bridges across gaps now existing between the different disciplines that study law.
Summary
Coherence of law is created in the writings of legal scholars who systematize rules and principles of law. Their pursuit of coherence is vital for the effectiveness of legal systems. However, coherence of law has almost not been analysed in a systematic, empirical way. The project will therefore develop a methodology that will address coherence across forms (‘sources’) of law (legislation, legal scholarship, case law, customs), across themes (e.g. criminal law and contracts) and across authors, and which will additionally encompass interaction with societal demand and contextual factors. The methodology will be ground-breaking because it will disentangle the concept of coherence into measurable modes of interconnectedness, weighing them together so as to assess (in)coherence at the level of the legal system. This methodology will constitute a stepping stone for a new field of dynamic coherence of law created through legal scholarship that will ultimately improve the quality of law. It will be founded on academic writings on law from the early modern period (ca. 1500 - ca. 1800) that concern the theme of collateral rights, that is, those rights facilitating expropriation of the assets of debtors in case of their default. Indications are that the impact of rules on collateral rights hinged on coherence as established in legal writings, and that in the period mentioned legal coherence for this theme was increasing. Coherence in development will be traced in the interpretations of legal scholars following on from interactions between scholarly writings, local law (bylaws, judgments) and commercial practice (contracts). Connections of rules and principles found will be presented in frames of analysis that cluster them along variables of context, time and source of law. The combination of legal analysis with a broad scope of coherence (cross-source, context-driven) will build bridges across gaps now existing between the different disciplines that study law.
Max ERC Funding
1 495 625 €
Duration
Start date: 2017-01-01, End date: 2021-12-31
Project acronym CMTaaRS
Project Defective protein translation as a pathogenic mechanism of peripheral neuropathy
Researcher (PI) Erik Jan Marthe STORKEBAUM
Host Institution (HI) STICHTING KATHOLIEKE UNIVERSITEIT
Call Details Consolidator Grant (CoG), LS5, ERC-2017-COG
Summary Familial forms of neurodegenerative diseases are caused by mutations in a single gene. It is unknown whether distinct mutations in the same gene or in functionally related genes cause disease through similar or disparate mechanisms. Furthermore, the precise molecular mechanisms underlying virtually all neurodegenerative disorders are poorly understood, and effective treatments are typically lacking.
This is also the case for Charcot-Marie-Tooth (CMT) peripheral neuropathy caused by mutations in five distinct tRNA synthetase (aaRS) genes. We previously generated Drosophila CMT-aaRS models and used a novel method for cell-type-specific labeling of newly synthesized proteins in vivo to show that impaired protein translation may represent a common pathogenic mechanism.
In this proposal, I aim to determine whether translation is also inhibited in CMT-aaRS mouse models, and whether all mutations cause disease through gain-of-toxic-function, or alternatively, whether some mutations act through a dominant-negative mechanism. In addition, I will evaluate whether all CMT-aaRS mutant proteins inhibit translation, and I will test the hypothesis, raised by our unpublished preliminary data shown here, that a defect in the transfer of the (aminoacylated) tRNA from the mutant synthetase to elongation factor eEF1A is the molecular mechanism underlying CMT-aaRS. Finally, I will validate the identified molecular mechanism in CMT-aaRS mouse models, as the most disease-relevant mammalian model.
I expect to elucidate whether all CMT-aaRS mutations cause disease through a common molecular mechanism that involves inhibition of translation. This is of key importance from a therapeutic perspective, as a common pathogenic mechanism allows for a unified therapeutic approach. Furthermore, this proposal has the potential to unravel the detailed molecular mechanism underlying CMT-aaRS, what would constitute a breakthrough and a requirement for rational drug design for this incurable disease.
Summary
Familial forms of neurodegenerative diseases are caused by mutations in a single gene. It is unknown whether distinct mutations in the same gene or in functionally related genes cause disease through similar or disparate mechanisms. Furthermore, the precise molecular mechanisms underlying virtually all neurodegenerative disorders are poorly understood, and effective treatments are typically lacking.
This is also the case for Charcot-Marie-Tooth (CMT) peripheral neuropathy caused by mutations in five distinct tRNA synthetase (aaRS) genes. We previously generated Drosophila CMT-aaRS models and used a novel method for cell-type-specific labeling of newly synthesized proteins in vivo to show that impaired protein translation may represent a common pathogenic mechanism.
In this proposal, I aim to determine whether translation is also inhibited in CMT-aaRS mouse models, and whether all mutations cause disease through gain-of-toxic-function, or alternatively, whether some mutations act through a dominant-negative mechanism. In addition, I will evaluate whether all CMT-aaRS mutant proteins inhibit translation, and I will test the hypothesis, raised by our unpublished preliminary data shown here, that a defect in the transfer of the (aminoacylated) tRNA from the mutant synthetase to elongation factor eEF1A is the molecular mechanism underlying CMT-aaRS. Finally, I will validate the identified molecular mechanism in CMT-aaRS mouse models, as the most disease-relevant mammalian model.
I expect to elucidate whether all CMT-aaRS mutations cause disease through a common molecular mechanism that involves inhibition of translation. This is of key importance from a therapeutic perspective, as a common pathogenic mechanism allows for a unified therapeutic approach. Furthermore, this proposal has the potential to unravel the detailed molecular mechanism underlying CMT-aaRS, what would constitute a breakthrough and a requirement for rational drug design for this incurable disease.
Max ERC Funding
2 000 000 €
Duration
Start date: 2018-06-01, End date: 2023-05-31
Project acronym CoCoUnit
Project CoCoUnit: An Energy-Efficient Processing Unit for Cognitive Computing
Researcher (PI) Antonio Maria Gonzalez Colas
Host Institution (HI) UNIVERSITAT POLITECNICA DE CATALUNYA
Call Details Advanced Grant (AdG), PE6, ERC-2018-ADG
Summary There is a fast-growing interest in extending the capabilities of computing systems to perform human-like tasks in an intelligent way. These technologies are usually referred to as cognitive computing. We envision a next revolution in computing in the forthcoming years that will be driven by deploying many “intelligent” devices around us in all kind of environments (work, entertainment, transportation, health care, etc.) backed up by “intelligent” servers in the cloud. These cognitive computing systems will provide new user experiences by delivering new services or improving the operational efficiency of existing ones, and altogether will enrich our lives and our economy.
A key characteristic of cognitive computing systems will be their capability to process in real time large amounts of data coming from audio and vision devices, and other type of sensors. This will demand a very high computing power but at the same time an extremely low energy consumption. This very challenging energy-efficiency requirement is a sine qua non to success not only for mobile and wearable systems, where power dissipation and cost budgets are very low, but also for large data centers where energy consumption is a main component of the total cost of ownership.
Current processor architectures (including general-purpose cores and GPUs) are not a good fit for this type of systems since they keep the same basic organization as early computers, which were mainly optimized for “number crunching”. CoCoUnit will take a disruptive direction by investigating unconventional architectures that can offer orders of magnitude better efficiency in terms of performance per energy and cost for cognitive computing tasks. The ultimate goal of this project is to devise a novel processing unit that will be integrated with the existing units of a processor (general-purpose cores and GPUs) and altogether will be able to deliver cognitive computing user experiences with extremely high energy-efficiency.
Summary
There is a fast-growing interest in extending the capabilities of computing systems to perform human-like tasks in an intelligent way. These technologies are usually referred to as cognitive computing. We envision a next revolution in computing in the forthcoming years that will be driven by deploying many “intelligent” devices around us in all kind of environments (work, entertainment, transportation, health care, etc.) backed up by “intelligent” servers in the cloud. These cognitive computing systems will provide new user experiences by delivering new services or improving the operational efficiency of existing ones, and altogether will enrich our lives and our economy.
A key characteristic of cognitive computing systems will be their capability to process in real time large amounts of data coming from audio and vision devices, and other type of sensors. This will demand a very high computing power but at the same time an extremely low energy consumption. This very challenging energy-efficiency requirement is a sine qua non to success not only for mobile and wearable systems, where power dissipation and cost budgets are very low, but also for large data centers where energy consumption is a main component of the total cost of ownership.
Current processor architectures (including general-purpose cores and GPUs) are not a good fit for this type of systems since they keep the same basic organization as early computers, which were mainly optimized for “number crunching”. CoCoUnit will take a disruptive direction by investigating unconventional architectures that can offer orders of magnitude better efficiency in terms of performance per energy and cost for cognitive computing tasks. The ultimate goal of this project is to devise a novel processing unit that will be integrated with the existing units of a processor (general-purpose cores and GPUs) and altogether will be able to deliver cognitive computing user experiences with extremely high energy-efficiency.
Max ERC Funding
2 498 661 €
Duration
Start date: 2019-09-01, End date: 2024-08-31
